AutoIHC-Analyzer: computer-assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers.

Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2-stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often results in interobserver variability. Further, the burden of increasing number of patients is a challenge for the pathologists. To address these challenges, there is an urgent need with a rapid HER2 cell membrane extraction method. The proposed study aims at developing an automated IHC scoring system, termed as AutoIHC-Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. A series of image processing approaches have been used to automatically extract the stained cells and membrane region, followed by automatic assessment of complete and broken membrane. Finally, a set of features are used to automatically classify the tissue under observation for the quantitative scoring as 0/1+, 2+ and 3+. In a set of surgically extracted cases of HER2-stained tissues, obtained from collaborative hospital for the testing and validation of the proposed approach AutoIHC-Analyzer and publicly available open source ImmunoMembrane software are compared for 90 set of randomly acquired images with the scores by expert pathologist where significant correlation is observed [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001)] respectively. The output shows promising quantification in automated scoring. LAY DESCRIPTION: In cancer prognosis amongst the patient of breast cancer, human epidermal growth factor receptor 2 (HER2) is used as Immunohistochemical (IHC) biomarker. The correct assessment of HER2 leads to the therapeutic decision making. In regular practice, the stained tissue sample is observed under a bright microscope and the expert pathologists score the sample as negative (0/1+), equivocal (2+) and positive (3+) case. The scoring is based on the standard guidelines relating the complete and broken cell membrane as well as intensity of staining in the membrane boundary. Such evaluation is time consuming, tedious and quite often results in interobserver variability. To assist in rapid HER2 cell membrane assessment, the proposed study aims at developing an automated IHC scoring system, termed as AutoIHC-Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. The input image is preprocessed using modified white patch and CMYK and RGB colour space were used in extracting the haematoxylin (negatively stained cells) and diaminobenzidine (DAB) stain observed in the tumour cell membrane. Segmentation and postprocessing are applied to create the masks for each of the stain channels. The membrane mask is then quantified as complete or broken using skeletonisation and morphological operations. Six set of features were assessed for the classification from a set of 180 training images. These features are: complete to broken membrane ratio, amount of stain using area of Blue and Saturation channels to the image size, DAB to haematoxylin ratio from segmented masks and average R, G and B from five largest blobs in segmented DAB-masked image. These features are then used in training the SVM classifier with Gaussian kernel using 5-fold cross-validation. The accuracy in the training sample is found to be 88.3%. The model is then used for 90 set of unknown test sample images and the final labelling of stained cells and HER2 scores (as 0/1+, 2+ and 3+) are compared with the ground truth, that is expert pathologists' score from the collaborative hospital. The test sample images were also fed to ImmunoMembrane software for a comparative assessment. The results from the proposed AutoIHC-Analyzer and ImmunoMembrane software were compared with the expert pathologists' score where significant agreement using Pearson's correlation coefficient [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001) respectively] is observed. The results from AutoIHC-Analyzer show promising quantitative assessment of HER2 scoring.

HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion.

In prognostic evaluation of breast cancer, immunohistochemical (IHC) marker human epidermal growth factor receptor 2 (HER2) is used for prognostic evaluation. Accurate assessment of HER2-stained tissue sample is essential in therapeutic decision making for the patients. In regular clinical settings, expert pathologists assess the HER2-stained tissue slide under microscope for manual scoring based on prior experience. Manual scoring is time consuming, tedious, and often prone to inter-observer variation among group of pathologists. With the recent advancement in the area of computer vision and deep learning, medical image analysis has got significant attention. A number of deep learning architectures have been proposed for classification of different image groups. These networks are also used for transfer learning to classify other image classes. In the presented study, a number of transfer learning architectures are used for HER2 scoring. Five pre-trained architectures viz. VGG16, VGG19, ResNet50, MobileNetV2, and NASNetMobile with decimating the fully connected layers to get 3-class classification have been used for the comparative assessment of the networks as well as further scoring of stained tissue sample image based on statistical voting using mode operator. HER2 Challenge dataset from Warwick University is used in this study. A total of 2130 image patches were extracted to generate the training dataset from 300 training images corresponding to 30 training cases. The output model is then tested on 800 new test image patches from 100 test images acquired from 10 test cases (different from training cases) to report the outcome results. The transfer learning models have shown significant accuracy with VGG19 showing the best accuracy for the test images. The accuracy is found to be 93%, which increases to 98% on the image-based scoring using statistical voting mechanism. The output shows a capable quantification pipeline in automated HER2 score generation.

Diagnostic and Prognostic Utility of Non-Invasive Multimodal Imaging in Chronic Wound Monitoring: a Systematic Review.

Monitoring chronic wound [CW] healing is a challenging issue for clinicians across the world. Moreover, the health and cost burden of CW are escalating at a disturbing rate due to a global rise in population of elderly and diabetic cases. The conventional approach includes visual contour, sketches, or more rarely tracings. However, such conventional techniques bring forth infection, pain, allergies. Furthermore, these methods are subjective as well as time-consuming. As such, nowadays, non-touching and non-invasive CW monitoring system based on imaging techniques are gaining importance. They not only reduce patients' discomfort but also provide rapid wound diagnosis and prognosis. This review provides a survey of different types of CW characteristics, their healing mechanism and the multimodal non-invasive imaging methods that have been used for their diagnosis and prognosis. Current clinical practices as well as personal health systems [m-health and e-health] for CW monitoring have been discussed.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water.

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Automated characterization and counting of Ki-67 protein for breast cancer prognosis: A quantitative immunohistochemistry approach.

Ki-67 protein expression plays an important role in predicting the proliferative status of tumour cells and deciding the future course of therapy in breast cancer. Immunohistochemical (IHC) determination of Ki-67 score or labelling index, by estimating the fraction of Ki67 positively stained tumour cells, is the most widely practiced method to assess tumour proliferation (Dowsett et al. 2011). Accurate manual counting of these cells (specifically nuclei) due to complex and dense distribution of cells, therefore, becomes critical and presents a major challenge to pathologists. In this paper, we suggest a hybrid clustering algorithm to quantify the proliferative index of breast cancer cells based on automated counting of Ki-67 nuclei. The proposed methodology initially pre-processes the IHC images of Ki-67 stained slides of breast cancer. The RGB images are converted to grey, L*a*b*, HSI, YCbCr, YIQ and XYZ colour space. All the stained cells are then characterized by two stage segmentation process. Fuzzy C-means quantifies all the stained cells as one cluster. The blue channel of the first stage output is given as input to k-means algorithm, which provides separate cluster for Ki-67 positive and negative cells. The count of positive and negative nuclei is used to calculate the F-measure for each colour space. A comparative study of our work with the expert opinion is studied to evaluate the error rate. The positive and negative nuclei detection results for all colour spaces are compared with the ground truth for validation and F-measure is calculated. The F-measure for L*a*b* colour space (0.8847) provides the best statistical result as compared to grey, HSI, YCbCr, YIQ and XYZ colour space. Further, a study is carried out to count nuclei manually and automatically from the proposed algorithm with an average error rate of 6.84% which is significant. The study provides an automated count of positive and negative nuclei using L*a*b*colour space and hybrid segmentation technique. Computerized evaluation of proliferation index can aid pathologist in assessing breast cancer severity. The proposed methodology, further, has the potential advantage of saving time and assisting in decision making over the present manual procedure and could evolve as an assistive pathological decision support system.